Overview

Dataset statistics

Number of variables25
Number of observations7752
Missing cells1248
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory200.0 B

Variable types

Numeric24
Categorical1

Warnings

Date has a high cardinality: 310 distinct values High cardinality
Present_Tmax is highly correlated with Present_Tmin and 4 other fieldsHigh correlation
Present_Tmin is highly correlated with Present_Tmax and 2 other fieldsHigh correlation
LDAPS_RHmin is highly correlated with LDAPS_RHmax and 5 other fieldsHigh correlation
LDAPS_RHmax is highly correlated with LDAPS_RHminHigh correlation
LDAPS_Tmax_lapse is highly correlated with Present_Tmax and 6 other fieldsHigh correlation
LDAPS_Tmin_lapse is highly correlated with Present_Tmax and 4 other fieldsHigh correlation
LDAPS_CC1 is highly correlated with LDAPS_RHmin and 2 other fieldsHigh correlation
LDAPS_CC2 is highly correlated with LDAPS_RHmin and 4 other fieldsHigh correlation
LDAPS_CC3 is highly correlated with LDAPS_RHmin and 5 other fieldsHigh correlation
LDAPS_CC4 is highly correlated with LDAPS_RHmin and 2 other fieldsHigh correlation
DEM is highly correlated with SlopeHigh correlation
Slope is highly correlated with DEMHigh correlation
Next_Tmax is highly correlated with Present_Tmax and 4 other fieldsHigh correlation
Next_Tmin is highly correlated with Present_Tmax and 4 other fieldsHigh correlation
Present_Tmax is highly correlated with Present_Tmin and 4 other fieldsHigh correlation
Present_Tmin is highly correlated with Present_Tmax and 2 other fieldsHigh correlation
LDAPS_RHmin is highly correlated with LDAPS_RHmax and 5 other fieldsHigh correlation
LDAPS_RHmax is highly correlated with LDAPS_RHminHigh correlation
LDAPS_Tmax_lapse is highly correlated with Present_Tmax and 6 other fieldsHigh correlation
LDAPS_Tmin_lapse is highly correlated with Present_Tmax and 4 other fieldsHigh correlation
LDAPS_CC1 is highly correlated with LDAPS_RHmin and 4 other fieldsHigh correlation
LDAPS_CC2 is highly correlated with LDAPS_RHmin and 6 other fieldsHigh correlation
LDAPS_CC3 is highly correlated with LDAPS_RHmin and 5 other fieldsHigh correlation
LDAPS_CC4 is highly correlated with LDAPS_CC2 and 2 other fieldsHigh correlation
LDAPS_PPT1 is highly correlated with LDAPS_CC1 and 2 other fieldsHigh correlation
LDAPS_PPT2 is highly correlated with LDAPS_RHmin and 3 other fieldsHigh correlation
LDAPS_PPT3 is highly correlated with LDAPS_CC3 and 1 other fieldsHigh correlation
LDAPS_PPT4 is highly correlated with LDAPS_CC4 and 1 other fieldsHigh correlation
DEM is highly correlated with SlopeHigh correlation
Slope is highly correlated with DEMHigh correlation
Next_Tmax is highly correlated with Present_Tmax and 3 other fieldsHigh correlation
Next_Tmin is highly correlated with Present_Tmax and 4 other fieldsHigh correlation
Present_Tmin is highly correlated with LDAPS_Tmin_lapse and 1 other fieldsHigh correlation
LDAPS_RHmin is highly correlated with LDAPS_CC2High correlation
LDAPS_Tmax_lapse is highly correlated with Next_TmaxHigh correlation
LDAPS_Tmin_lapse is highly correlated with Present_Tmin and 1 other fieldsHigh correlation
LDAPS_CC1 is highly correlated with LDAPS_CC2 and 1 other fieldsHigh correlation
LDAPS_CC2 is highly correlated with LDAPS_RHmin and 3 other fieldsHigh correlation
LDAPS_CC3 is highly correlated with LDAPS_CC2 and 1 other fieldsHigh correlation
LDAPS_CC4 is highly correlated with LDAPS_CC3High correlation
LDAPS_PPT1 is highly correlated with LDAPS_CC1 and 1 other fieldsHigh correlation
LDAPS_PPT2 is highly correlated with LDAPS_CC2 and 1 other fieldsHigh correlation
DEM is highly correlated with SlopeHigh correlation
Slope is highly correlated with DEMHigh correlation
Next_Tmax is highly correlated with LDAPS_Tmax_lapseHigh correlation
Next_Tmin is highly correlated with Present_Tmin and 1 other fieldsHigh correlation
Present_Tmax is highly correlated with Present_Tmin and 4 other fieldsHigh correlation
Present_Tmin is highly correlated with Present_Tmax and 5 other fieldsHigh correlation
LDAPS_LH is highly correlated with lat and 1 other fieldsHigh correlation
LDAPS_PPT1 is highly correlated with LDAPS_CC1 and 1 other fieldsHigh correlation
Slope is highly correlated with lat and 3 other fieldsHigh correlation
lat is highly correlated with LDAPS_LH and 4 other fieldsHigh correlation
LDAPS_RHmax is highly correlated with LDAPS_RHminHigh correlation
LDAPS_CC3 is highly correlated with LDAPS_Tmax_lapse and 6 other fieldsHigh correlation
LDAPS_Tmax_lapse is highly correlated with Present_Tmax and 10 other fieldsHigh correlation
LDAPS_CC4 is highly correlated with LDAPS_CC3 and 5 other fieldsHigh correlation
lon is highly correlated with Slope and 3 other fieldsHigh correlation
LDAPS_WS is highly correlated with LDAPS_Tmax_lapse and 1 other fieldsHigh correlation
Next_Tmin is highly correlated with Present_Tmax and 5 other fieldsHigh correlation
LDAPS_CC1 is highly correlated with LDAPS_PPT1 and 4 other fieldsHigh correlation
DEM is highly correlated with Slope and 3 other fieldsHigh correlation
station is highly correlated with LDAPS_LH and 4 other fieldsHigh correlation
Solar radiation is highly correlated with Present_Tmin and 3 other fieldsHigh correlation
LDAPS_Tmin_lapse is highly correlated with Present_Tmax and 5 other fieldsHigh correlation
LDAPS_RHmin is highly correlated with LDAPS_RHmax and 6 other fieldsHigh correlation
LDAPS_PPT3 is highly correlated with LDAPS_CC3High correlation
LDAPS_CC2 is highly correlated with LDAPS_CC3 and 6 other fieldsHigh correlation
Next_Tmax is highly correlated with Present_Tmax and 10 other fieldsHigh correlation
LDAPS_PPT2 is highly correlated with LDAPS_PPT1 and 1 other fieldsHigh correlation
LDAPS_PPT4 is highly correlated with LDAPS_CC4High correlation
Date is uniformly distributed Uniform
LDAPS_CC1 has 108 (1.4%) zeros Zeros
LDAPS_CC2 has 95 (1.2%) zeros Zeros
LDAPS_CC4 has 137 (1.8%) zeros Zeros
LDAPS_PPT1 has 4844 (62.5%) zeros Zeros
LDAPS_PPT2 has 5151 (66.4%) zeros Zeros
LDAPS_PPT3 has 5294 (68.3%) zeros Zeros
LDAPS_PPT4 has 5751 (74.2%) zeros Zeros

Reproduction

Analysis started2021-08-15 01:28:31.844726
Analysis finished2021-08-15 01:30:42.603617
Duration2 minutes and 10.76 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

station
Real number (ℝ≥0)

HIGH CORRELATION

Distinct25
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:42.731280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median13
Q319
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.211567828
Coefficient of variation (CV)0.5547359868
Kurtosis-1.203848596
Mean13
Median Absolute Deviation (MAD)6
Skewness0
Sum100750
Variance52.00671054
MonotonicityNot monotonic
2021-08-15T09:30:42.883901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
25310
 
4.0%
23310
 
4.0%
2310
 
4.0%
3310
 
4.0%
4310
 
4.0%
5310
 
4.0%
6310
 
4.0%
7310
 
4.0%
8310
 
4.0%
9310
 
4.0%
Other values (15)4650
60.0%
ValueCountFrequency (%)
1310
4.0%
2310
4.0%
3310
4.0%
4310
4.0%
5310
4.0%
6310
4.0%
7310
4.0%
8310
4.0%
9310
4.0%
10310
4.0%
ValueCountFrequency (%)
25310
4.0%
24310
4.0%
23310
4.0%
22310
4.0%
21310
4.0%
20310
4.0%
19310
4.0%
18310
4.0%
17310
4.0%
16310
4.0%

Date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct310
Distinct (%)4.0%
Missing2
Missing (%)< 0.1%
Memory size60.7 KiB
8/20/2015
 
25
8/24/2016
 
25
8/5/2014
 
25
7/19/2015
 
25
7/26/2016
 
25
Other values (305)
7625 

Length

Max length9
Median length9
Mean length8.709677419
Min length8

Characters and Unicode

Total characters67500
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6/30/2013
2nd row6/30/2013
3rd row6/30/2013
4th row6/30/2013
5th row6/30/2013

Common Values

ValueCountFrequency (%)
8/20/201525
 
0.3%
8/24/201625
 
0.3%
8/5/201425
 
0.3%
7/19/201525
 
0.3%
7/26/201625
 
0.3%
7/31/201625
 
0.3%
8/30/201325
 
0.3%
7/11/201625
 
0.3%
8/22/201725
 
0.3%
8/4/201625
 
0.3%
Other values (300)7500
96.7%

Length

2021-08-15T09:30:43.244901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8/20/201525
 
0.3%
8/4/201625
 
0.3%
7/19/201525
 
0.3%
7/26/201625
 
0.3%
7/31/201625
 
0.3%
8/30/201325
 
0.3%
7/11/201625
 
0.3%
8/22/201725
 
0.3%
8/24/201625
 
0.3%
8/23/201525
 
0.3%
Other values (300)7500
96.8%

Most occurring characters

ValueCountFrequency (%)
/15500
23.0%
111125
16.5%
211000
16.3%
08625
12.8%
76175
 
9.1%
84500
 
6.7%
32800
 
4.1%
62425
 
3.6%
42300
 
3.4%
52300
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number52000
77.0%
Other Punctuation15500
 
23.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
111125
21.4%
211000
21.2%
08625
16.6%
76175
11.9%
84500
8.7%
32800
 
5.4%
62425
 
4.7%
42300
 
4.4%
52300
 
4.4%
9750
 
1.4%
Other Punctuation
ValueCountFrequency (%)
/15500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common67500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/15500
23.0%
111125
16.5%
211000
16.3%
08625
12.8%
76175
 
9.1%
84500
 
6.7%
32800
 
4.1%
62425
 
3.6%
42300
 
3.4%
52300
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII67500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/15500
23.0%
111125
16.5%
211000
16.3%
08625
12.8%
76175
 
9.1%
84500
 
6.7%
32800
 
4.1%
62425
 
3.6%
42300
 
3.4%
52300
 
3.4%

Present_Tmax
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct167
Distinct (%)2.2%
Missing70
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean29.7682114
Minimum20
Maximum37.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:43.399486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile24.6
Q127.8
median29.9
Q332
95-th percentile34.3
Maximum37.6
Range17.6
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation2.96999878
Coefficient of variation (CV)0.09977081725
Kurtosis-0.4127682457
Mean29.7682114
Median Absolute Deviation (MAD)2.1
Skewness-0.2629423693
Sum228679.4
Variance8.820892752
MonotonicityNot monotonic
2021-08-15T09:30:43.577013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.4112
 
1.4%
29.4111
 
1.4%
29.1108
 
1.4%
29.7107
 
1.4%
30.6105
 
1.4%
31.9105
 
1.4%
29.2105
 
1.4%
30101
 
1.3%
30.3101
 
1.3%
32.2100
 
1.3%
Other values (157)6627
85.5%
ValueCountFrequency (%)
202
< 0.1%
20.11
< 0.1%
20.31
< 0.1%
20.41
< 0.1%
20.62
< 0.1%
20.71
< 0.1%
21.21
< 0.1%
21.42
< 0.1%
21.52
< 0.1%
21.62
< 0.1%
ValueCountFrequency (%)
37.62
 
< 0.1%
37.52
 
< 0.1%
37.22
 
< 0.1%
37.11
 
< 0.1%
36.91
 
< 0.1%
36.83
< 0.1%
36.76
0.1%
36.62
 
< 0.1%
36.54
0.1%
36.41
 
< 0.1%

Present_Tmin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct155
Distinct (%)2.0%
Missing70
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean23.22505858
Minimum11.3
Maximum29.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:43.815376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum11.3
5-th percentile19.1
Q121.7
median23.4
Q324.9
95-th percentile27
Maximum29.9
Range18.6
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation2.413961103
Coefficient of variation (CV)0.1039377832
Kurtosis0.1976512112
Mean23.22505858
Median Absolute Deviation (MAD)1.6
Skewness-0.3658751814
Sum178414.9
Variance5.827208205
MonotonicityNot monotonic
2021-08-15T09:30:44.028839image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24161
 
2.1%
23.8153
 
2.0%
23.1144
 
1.9%
23.5143
 
1.8%
23.3142
 
1.8%
23.7141
 
1.8%
23.9138
 
1.8%
23.6137
 
1.8%
23.4136
 
1.8%
23135
 
1.7%
Other values (145)6252
80.7%
ValueCountFrequency (%)
11.32
< 0.1%
13.51
< 0.1%
13.61
< 0.1%
14.11
< 0.1%
14.21
< 0.1%
14.31
< 0.1%
14.42
< 0.1%
14.61
< 0.1%
14.82
< 0.1%
151
< 0.1%
ValueCountFrequency (%)
29.92
 
< 0.1%
29.71
 
< 0.1%
29.61
 
< 0.1%
29.22
 
< 0.1%
29.11
 
< 0.1%
292
 
< 0.1%
28.94
0.1%
28.83
< 0.1%
28.73
< 0.1%
28.66
0.1%

LDAPS_RHmin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7672
Distinct (%)99.9%
Missing75
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean56.75937215
Minimum19.79466629
Maximum98.5247345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:44.228305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum19.79466629
5-th percentile34.61397323
Q145.96354294
median55.03902435
Q367.19005585
95-th percentile82.83589783
Maximum98.5247345
Range78.73006821
Interquartile range (IQR)21.22651291

Descriptive statistics

Standard deviation14.66811137
Coefficient of variation (CV)0.2584262442
Kurtosis-0.5208967924
Mean56.75937215
Median Absolute Deviation (MAD)10.34185028
Skewness0.2987645035
Sum435741.7
Variance215.1534911
MonotonicityNot monotonic
2021-08-15T09:30:44.408821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.658088682
 
< 0.1%
51.810596472
 
< 0.1%
98.52473452
 
< 0.1%
77.030349732
 
< 0.1%
19.794666292
 
< 0.1%
75.352851871
 
< 0.1%
39.925216671
 
< 0.1%
58.979747771
 
< 0.1%
53.686511991
 
< 0.1%
41.498203281
 
< 0.1%
Other values (7662)7662
98.8%
(Missing)75
 
1.0%
ValueCountFrequency (%)
19.794666292
< 0.1%
20.090684891
< 0.1%
21.00970841
< 0.1%
21.468114851
< 0.1%
21.469881061
< 0.1%
21.529964451
< 0.1%
21.663679121
< 0.1%
22.087886811
< 0.1%
22.338659291
< 0.1%
22.426591871
< 0.1%
ValueCountFrequency (%)
98.52473452
< 0.1%
98.194175721
< 0.1%
97.482574461
< 0.1%
96.933921811
< 0.1%
96.564727781
< 0.1%
96.169815061
< 0.1%
96.087348941
< 0.1%
95.818939211
< 0.1%
95.803283691
< 0.1%
95.752685551
< 0.1%

LDAPS_RHmax
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7664
Distinct (%)99.8%
Missing75
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean88.37480389
Minimum58.93628311
Maximum100.0001526
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:44.608287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum58.93628311
5-th percentile74.3182373
Q184.22286224
median89.79347992
Q393.74362946
95-th percentile97.73377991
Maximum100.0001526
Range41.06386949
Interquartile range (IQR)9.52076722

Descriptive statistics

Standard deviation7.192004103
Coefficient of variation (CV)0.08138070792
Kurtosis0.3621454393
Mean88.37480389
Median Absolute Deviation (MAD)4.50279999
Skewness-0.8508699656
Sum678453.3694
Variance51.72492301
MonotonicityNot monotonic
2021-08-15T09:30:44.773845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85.863731382
 
< 0.1%
96.058418272
 
< 0.1%
96.525199892
 
< 0.1%
99.251968382
 
< 0.1%
88.938194272
 
< 0.1%
88.87660982
 
< 0.1%
91.206520082
 
< 0.1%
91.776145942
 
< 0.1%
94.79596712
 
< 0.1%
91.878173832
 
< 0.1%
Other values (7654)7657
98.8%
(Missing)75
 
1.0%
ValueCountFrequency (%)
58.936283112
< 0.1%
60.728260041
< 0.1%
61.340576171
< 0.1%
61.5890771
< 0.1%
62.045112611
< 0.1%
62.645523071
< 0.1%
62.742103581
< 0.1%
62.900074011
< 0.1%
62.984230041
< 0.1%
63.087425231
< 0.1%
ValueCountFrequency (%)
100.00015262
< 0.1%
99.999420171
< 0.1%
99.999008181
< 0.1%
99.996887211
< 0.1%
99.995170591
< 0.1%
99.992660521
< 0.1%
99.992424011
< 0.1%
99.987289431
< 0.1%
99.985824591
< 0.1%
99.985565191
< 0.1%

LDAPS_Tmax_lapse
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7675
Distinct (%)> 99.9%
Missing75
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean29.61344654
Minimum17.62495378
Maximum38.54225522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:44.939368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum17.62495378
5-th percentile24.53059542
Q127.67349866
median29.70342642
Q331.71044989
95-th percentile34.30162848
Maximum38.54225522
Range20.91730144
Interquartile range (IQR)4.03695123

Descriptive statistics

Standard deviation2.947191005
Coefficient of variation (CV)0.09952205329
Kurtosis-0.03238064427
Mean29.61344654
Median Absolute Deviation (MAD)2.02097205
Skewness-0.2267749172
Sum227342.4291
Variance8.68593482
MonotonicityNot monotonic
2021-08-15T09:30:45.091992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.624953782
 
< 0.1%
38.542255222
 
< 0.1%
30.874998851
 
< 0.1%
28.708004031
 
< 0.1%
32.201413481
 
< 0.1%
23.508735771
 
< 0.1%
35.880887011
 
< 0.1%
27.767513251
 
< 0.1%
30.31994561
 
< 0.1%
30.574987091
 
< 0.1%
Other values (7665)7665
98.9%
(Missing)75
 
1.0%
ValueCountFrequency (%)
17.624953782
< 0.1%
17.835557571
< 0.1%
18.308505811
< 0.1%
19.173606481
< 0.1%
19.213062481
< 0.1%
19.244076591
< 0.1%
19.300222811
< 0.1%
19.388251271
< 0.1%
19.592573131
< 0.1%
19.770220781
< 0.1%
ValueCountFrequency (%)
38.542255222
< 0.1%
38.140534491
< 0.1%
38.028742891
< 0.1%
37.438653841
< 0.1%
37.353029791
< 0.1%
37.213423261
< 0.1%
37.136138681
< 0.1%
37.050300591
< 0.1%
36.959192761
< 0.1%
36.930922751
< 0.1%

LDAPS_Tmin_lapse
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7675
Distinct (%)> 99.9%
Missing75
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean23.51258878
Minimum14.27264631
Maximum29.61934244
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:45.270483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum14.27264631
5-th percentile19.4488671
Q122.08973876
median23.76019884
Q325.15290854
95-th percentile27.01916987
Maximum29.61934244
Range15.34669613
Interquartile range (IQR)3.06316978

Descriptive statistics

Standard deviation2.345347303
Coefficient of variation (CV)0.09974857831
Kurtosis0.5024948132
Mean23.51258878
Median Absolute Deviation (MAD)1.49646783
Skewness-0.578943259
Sum180506.144
Variance5.50065397
MonotonicityNot monotonic
2021-08-15T09:30:45.464994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.272646312
 
< 0.1%
29.619342442
 
< 0.1%
22.604264941
 
< 0.1%
26.177076191
 
< 0.1%
24.143785411
 
< 0.1%
27.646552891
 
< 0.1%
24.018823541
 
< 0.1%
23.411361411
 
< 0.1%
21.870333981
 
< 0.1%
23.473680811
 
< 0.1%
Other values (7665)7665
98.9%
(Missing)75
 
1.0%
ValueCountFrequency (%)
14.272646312
< 0.1%
14.301375421
< 0.1%
14.443233171
< 0.1%
14.500812531
< 0.1%
14.720028881
< 0.1%
14.771416241
< 0.1%
14.79108171
< 0.1%
14.892483871
< 0.1%
14.893155391
< 0.1%
14.907923321
< 0.1%
ValueCountFrequency (%)
29.619342442
< 0.1%
29.604480231
< 0.1%
29.564647171
< 0.1%
29.527612241
< 0.1%
29.520258781
< 0.1%
29.084492221
< 0.1%
29.064993371
< 0.1%
28.982639191
< 0.1%
28.972176311
< 0.1%
28.900009811
< 0.1%

LDAPS_WS
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7675
Distinct (%)> 99.9%
Missing75
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean7.09787457
Minimum2.882579625
Maximum21.85762099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:45.653457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.882579625
5-th percentile4.54411884
Q15.678705116
median6.547470301
Q38.032276144
95-th percentile11.41874935
Maximum21.85762099
Range18.97504137
Interquartile range (IQR)2.353571028

Descriptive statistics

Standard deviation2.183835979
Coefficient of variation (CV)0.3076746366
Kurtosis3.709967069
Mean7.09787457
Median Absolute Deviation (MAD)1.078852932
Skewness1.571580883
Sum54490.38307
Variance4.769139582
MonotonicityNot monotonic
2021-08-15T09:30:45.839957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8825796252
 
< 0.1%
21.857620992
 
< 0.1%
10.157698891
 
< 0.1%
5.1452324291
 
< 0.1%
4.2040299181
 
< 0.1%
5.4016676841
 
< 0.1%
8.5384078221
 
< 0.1%
6.3815395041
 
< 0.1%
8.1047320271
 
< 0.1%
6.1981564161
 
< 0.1%
Other values (7665)7665
98.9%
(Missing)75
 
1.0%
ValueCountFrequency (%)
2.8825796252
< 0.1%
3.0079538971
< 0.1%
3.0131411551
< 0.1%
3.1800288511
< 0.1%
3.2952041231
< 0.1%
3.3802521631
< 0.1%
3.4030593681
< 0.1%
3.4114691121
< 0.1%
3.4192722891
< 0.1%
3.4446022011
< 0.1%
ValueCountFrequency (%)
21.857620992
< 0.1%
20.85416421
< 0.1%
20.113253431
< 0.1%
20.048248931
< 0.1%
19.34172131
< 0.1%
19.068184971
< 0.1%
18.112303971
< 0.1%
18.043689891
< 0.1%
17.912887331
< 0.1%
17.869234881
< 0.1%

LDAPS_LH
Real number (ℝ)

HIGH CORRELATION

Distinct7675
Distinct (%)> 99.9%
Missing75
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean62.50501891
Minimum-13.60321209
Maximum213.4140062
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)0.1%
Memory size60.7 KiB
2021-08-15T09:30:46.041452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-13.60321209
5-th percentile15.19832322
Q137.26675278
median56.86548154
Q384.22361625
95-th percentile125.359223
Maximum213.4140062
Range227.0172183
Interquartile range (IQR)46.95686347

Descriptive statistics

Standard deviation33.73058877
Coefficient of variation (CV)0.5396460853
Kurtosis0.1039469093
Mean62.50501891
Median Absolute Deviation (MAD)22.60446922
Skewness0.6704914371
Sum479851.0302
Variance1137.752619
MonotonicityNot monotonic
2021-08-15T09:30:46.198035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-13.603212092
 
< 0.1%
213.41400622
 
< 0.1%
52.898935871
 
< 0.1%
58.594890511
 
< 0.1%
35.230479351
 
< 0.1%
21.824042841
 
< 0.1%
87.453063891
 
< 0.1%
30.491393411
 
< 0.1%
51.079010881
 
< 0.1%
94.809865121
 
< 0.1%
Other values (7665)7665
98.9%
(Missing)75
 
1.0%
ValueCountFrequency (%)
-13.603212092
< 0.1%
-8.5690425421
< 0.1%
-3.753933211
< 0.1%
3.7048824431
< 0.1%
5.4043793171
< 0.1%
6.1169246141
< 0.1%
6.1711653511
< 0.1%
6.3701180521
< 0.1%
6.7689102991
< 0.1%
7.0885084371
< 0.1%
ValueCountFrequency (%)
213.41400622
< 0.1%
208.23469761
< 0.1%
203.7060621
< 0.1%
200.4114511
< 0.1%
189.22424611
< 0.1%
185.39524451
< 0.1%
182.61404751
< 0.1%
179.68156971
< 0.1%
178.40345681
< 0.1%
176.88028531
< 0.1%

LDAPS_CC1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7569
Distinct (%)98.6%
Missing75
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.3687735856
Minimum0
Maximum0.967277328
Zeros108
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:46.371569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0209252102
Q10.14665401
median0.315696837
Q30.575488778
95-th percentile0.8366165616
Maximum0.967277328
Range0.967277328
Interquartile range (IQR)0.428834768

Descriptive statistics

Standard deviation0.2624575535
Coefficient of variation (CV)0.7117037765
Kurtosis-0.9223608894
Mean0.3687735856
Median Absolute Deviation (MAD)0.196663176
Skewness0.4572306412
Sum2831.074817
Variance0.06888396741
MonotonicityNot monotonic
2021-08-15T09:30:46.557038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0108
 
1.4%
0.9672773282
 
< 0.1%
0.7262341371
 
< 0.1%
0.2718882331
 
< 0.1%
0.0512298621
 
< 0.1%
0.294329291
 
< 0.1%
0.6114429751
 
< 0.1%
0.4326174511
 
< 0.1%
0.4552142811
 
< 0.1%
0.2237800941
 
< 0.1%
Other values (7559)7559
97.5%
(Missing)75
 
1.0%
ValueCountFrequency (%)
0108
1.4%
2.06 × 10-61
 
< 0.1%
2.81 × 10-61
 
< 0.1%
6.36 × 10-61
 
< 0.1%
1.06 × 10-51
 
< 0.1%
4.83 × 10-51
 
< 0.1%
5.14 × 10-51
 
< 0.1%
6.1 × 10-51
 
< 0.1%
6.36 × 10-51
 
< 0.1%
6.72 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.9672773282
< 0.1%
0.9623011911
< 0.1%
0.9591298961
< 0.1%
0.9542416631
< 0.1%
0.953767211
< 0.1%
0.9522360271
< 0.1%
0.9518991911
< 0.1%
0.9503215091
< 0.1%
0.9475252791
< 0.1%
0.9471976531
< 0.1%

LDAPS_CC2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7582
Distinct (%)98.8%
Missing75
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.3560804373
Minimum0
Maximum0.96835306
Zeros95
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:46.782435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0091249346
Q10.140614839
median0.312421315
Q30.558694442
95-th percentile0.8296098904
Maximum0.96835306
Range0.96835306
Interquartile range (IQR)0.418079603

Descriptive statistics

Standard deviation0.2580612293
Coefficient of variation (CV)0.7247273432
Kurtosis-0.8511465852
Mean0.3560804373
Median Absolute Deviation (MAD)0.197461964
Skewness0.4700602065
Sum2733.629517
Variance0.06659559805
MonotonicityNot monotonic
2021-08-15T09:30:46.962987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
095
 
1.2%
0.968353062
 
< 0.1%
0.1956559451
 
< 0.1%
0.1831106111
 
< 0.1%
0.0789023481
 
< 0.1%
0.3731789571
 
< 0.1%
0.8876678711
 
< 0.1%
0.8081598841
 
< 0.1%
0.6215711321
 
< 0.1%
0.6005879841
 
< 0.1%
Other values (7572)7572
97.7%
(Missing)75
 
1.0%
ValueCountFrequency (%)
095
1.2%
1.47 × 10-71
 
< 0.1%
5.34 × 10-71
 
< 0.1%
4.97 × 10-61
 
< 0.1%
5.62 × 10-61
 
< 0.1%
9.02 × 10-61
 
< 0.1%
1.11 × 10-51
 
< 0.1%
1.19 × 10-51
 
< 0.1%
1.59 × 10-51
 
< 0.1%
1.81 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.968353062
< 0.1%
0.9669970211
< 0.1%
0.9666374361
< 0.1%
0.9661862251
< 0.1%
0.9658887351
< 0.1%
0.9640208811
< 0.1%
0.9632067711
< 0.1%
0.9625791051
< 0.1%
0.9600998161
< 0.1%
0.9598513641
< 0.1%

LDAPS_CC3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7599
Distinct (%)99.0%
Missing75
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.3184039558
Minimum0
Maximum0.983788755
Zeros76
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:47.146463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0085693266
Q10.101387577
median0.262554604
Q30.496703465
95-th percentile0.7871236342
Maximum0.983788755
Range0.983788755
Interquartile range (IQR)0.395315888

Descriptive statistics

Standard deviation0.2503621985
Coefficient of variation (CV)0.7863036685
Kurtosis-0.6300587937
Mean0.3184039558
Median Absolute Deviation (MAD)0.183656068
Skewness0.6376295817
Sum2444.387169
Variance0.06268123046
MonotonicityNot monotonic
2021-08-15T09:30:47.343935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
076
 
1.0%
5.81 × 10-72
 
< 0.1%
0.0008680562
 
< 0.1%
0.9837887552
 
< 0.1%
0.2824153991
 
< 0.1%
0.3415895741
 
< 0.1%
0.3473613961
 
< 0.1%
0.1940175011
 
< 0.1%
0.7303486771
 
< 0.1%
0.0490670751
 
< 0.1%
Other values (7589)7589
97.9%
(Missing)75
 
1.0%
ValueCountFrequency (%)
076
1.0%
5.81 × 10-72
 
< 0.1%
5.85 × 10-71
 
< 0.1%
2.26 × 10-61
 
< 0.1%
4.95 × 10-61
 
< 0.1%
6.09 × 10-61
 
< 0.1%
8.29 × 10-61
 
< 0.1%
1.03 × 10-51
 
< 0.1%
1.05 × 10-51
 
< 0.1%
1.96 × 10-51
 
< 0.1%
ValueCountFrequency (%)
0.9837887552
< 0.1%
0.9837565591
< 0.1%
0.9816517271
< 0.1%
0.9801757771
< 0.1%
0.9774262541
< 0.1%
0.97601771
< 0.1%
0.9723899861
< 0.1%
0.971772291
< 0.1%
0.9715066721
< 0.1%
0.9714601111
< 0.1%

LDAPS_CC4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7524
Distinct (%)98.0%
Missing75
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.2991913889
Minimum0
Maximum0.974709524
Zeros137
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:47.564377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0036280888
Q10.081531957
median0.227664469
Q30.499489186
95-th percentile0.7784244252
Maximum0.974709524
Range0.974709524
Interquartile range (IQR)0.417957229

Descriptive statistics

Standard deviation0.2543475065
Coefficient of variation (CV)0.8501164001
Kurtosis-0.7168758279
Mean0.2991913889
Median Absolute Deviation (MAD)0.179763542
Skewness0.663250982
Sum2296.892293
Variance0.06469265406
MonotonicityNot monotonic
2021-08-15T09:30:47.763844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0137
 
1.8%
0.0026041677
 
0.1%
0.0052083333
 
< 0.1%
0.0017361113
 
< 0.1%
1.45 × 10-62
 
< 0.1%
2.06 × 10-62
 
< 0.1%
0.0043402782
 
< 0.1%
0.3730250142
 
< 0.1%
0.9747095242
 
< 0.1%
5.18 × 10-52
 
< 0.1%
Other values (7514)7515
96.9%
(Missing)75
 
1.0%
ValueCountFrequency (%)
0137
1.8%
2.99 × 10-81
 
< 0.1%
3.01 × 10-71
 
< 0.1%
4.51 × 10-71
 
< 0.1%
5.4 × 10-71
 
< 0.1%
8.72 × 10-71
 
< 0.1%
9.69 × 10-71
 
< 0.1%
1.35 × 10-61
 
< 0.1%
1.45 × 10-62
 
< 0.1%
2.06 × 10-62
 
< 0.1%
ValueCountFrequency (%)
0.9747095242
< 0.1%
0.9673491291
< 0.1%
0.9626402091
< 0.1%
0.9615063371
< 0.1%
0.9578884771
< 0.1%
0.95634831
< 0.1%
0.9540782311
< 0.1%
0.9532074561
< 0.1%
0.9492933371
< 0.1%
0.9489082511
< 0.1%

LDAPS_PPT1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2812
Distinct (%)36.6%
Missing75
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.5919945267
Minimum0
Maximum23.70154408
Zeros4844
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:47.947358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.052524642
95-th percentile3.619205656
Maximum23.70154408
Range23.70154408
Interquartile range (IQR)0.052524642

Descriptive statistics

Standard deviation1.94576811
Coefficient of variation (CV)3.286800844
Kurtosis36.63231333
Mean0.5919945267
Median Absolute Deviation (MAD)0
Skewness5.367675474
Sum4544.741981
Variance3.786013538
MonotonicityNot monotonic
2021-08-15T09:30:48.115902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04844
62.5%
0.00195312511
 
0.1%
0.0026041675
 
0.1%
0.0017337883
 
< 0.1%
0.0001149482
 
< 0.1%
0.0021988832
 
< 0.1%
23.701544082
 
< 0.1%
0.0004587182
 
< 0.1%
4.84 × 10-52
 
< 0.1%
0.0005583722
 
< 0.1%
Other values (2802)2802
36.1%
(Missing)75
 
1.0%
ValueCountFrequency (%)
04844
62.5%
1.51 × 10-51
 
< 0.1%
1.89 × 10-51
 
< 0.1%
3.15 × 10-51
 
< 0.1%
3.45 × 10-51
 
< 0.1%
3.78 × 10-51
 
< 0.1%
3.88 × 10-51
 
< 0.1%
4.02 × 10-51
 
< 0.1%
4.84 × 10-52
 
< 0.1%
6.35 × 10-51
 
< 0.1%
ValueCountFrequency (%)
23.701544082
< 0.1%
23.223036041
< 0.1%
22.36088571
< 0.1%
20.941284041
< 0.1%
20.65309031
< 0.1%
19.332735481
< 0.1%
18.88732191
< 0.1%
18.878814941
< 0.1%
18.841569321
< 0.1%
18.819171641
< 0.1%

LDAPS_PPT2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2510
Distinct (%)32.7%
Missing75
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.4850025591
Minimum0
Maximum21.62166078
Zeros5151
Zeros (%)66.4%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:48.295423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.018364114
95-th percentile3.369460909
Maximum21.62166078
Range21.62166078
Interquartile range (IQR)0.018364114

Descriptive statistics

Standard deviation1.762807246
Coefficient of variation (CV)3.634634937
Kurtosis42.29976986
Mean0.4850025591
Median Absolute Deviation (MAD)0
Skewness5.747359562
Sum3723.364646
Variance3.107489387
MonotonicityNot monotonic
2021-08-15T09:30:48.458951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05151
66.4%
0.0019531257
 
0.1%
0.00078083
 
< 0.1%
4.02 × 10-53
 
< 0.1%
1.41 × 10-52
 
< 0.1%
0.0017035632
 
< 0.1%
0.0013947532
 
< 0.1%
21.621660782
 
< 0.1%
0.0005256392
 
< 0.1%
0.0026041672
 
< 0.1%
Other values (2500)2501
32.3%
(Missing)75
 
1.0%
ValueCountFrequency (%)
05151
66.4%
1.41 × 10-52
 
< 0.1%
1.65 × 10-51
 
< 0.1%
1.79 × 10-51
 
< 0.1%
1.92 × 10-51
 
< 0.1%
2.36 × 10-51
 
< 0.1%
3.46 × 10-51
 
< 0.1%
4.02 × 10-53
 
< 0.1%
4.15 × 10-51
 
< 0.1%
5.2 × 10-51
 
< 0.1%
ValueCountFrequency (%)
21.621660782
< 0.1%
21.276557581
< 0.1%
20.942564751
< 0.1%
20.414146741
< 0.1%
20.369826021
< 0.1%
20.150520481
< 0.1%
19.801453131
< 0.1%
19.145453691
< 0.1%
18.428032521
< 0.1%
18.150070391
< 0.1%

LDAPS_PPT3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2356
Distinct (%)30.7%
Missing75
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.2781996393
Minimum0
Maximum15.84123484
Zeros5294
Zeros (%)68.3%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:48.637475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.00789632
95-th percentile1.414518103
Maximum15.84123484
Range15.84123484
Interquartile range (IQR)0.00789632

Descriptive statistics

Standard deviation1.161808677
Coefficient of variation (CV)4.176168885
Kurtosis49.99251679
Mean0.2781996393
Median Absolute Deviation (MAD)0
Skewness6.425829485
Sum2135.738631
Variance1.349799403
MonotonicityNot monotonic
2021-08-15T09:30:49.298738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05294
68.3%
0.00195312510
 
0.1%
0.0008517814
 
0.1%
0.0026041673
 
< 0.1%
0.0003136172
 
< 0.1%
0.0003061062
 
< 0.1%
2.47 × 10-52
 
< 0.1%
0.0008635462
 
< 0.1%
2.05 × 10-52
 
< 0.1%
0.0005583642
 
< 0.1%
Other values (2346)2354
30.4%
(Missing)75
 
1.0%
ValueCountFrequency (%)
05294
68.3%
2.05 × 10-52
 
< 0.1%
2.36 × 10-51
 
< 0.1%
2.47 × 10-52
 
< 0.1%
2.84 × 10-51
 
< 0.1%
3.07 × 10-51
 
< 0.1%
3.29 × 10-51
 
< 0.1%
3.78 × 10-52
 
< 0.1%
4.02 × 10-51
 
< 0.1%
4.41 × 10-51
 
< 0.1%
ValueCountFrequency (%)
15.841234842
< 0.1%
15.13807171
< 0.1%
14.110428791
< 0.1%
14.046389261
< 0.1%
13.759697281
< 0.1%
13.692943931
< 0.1%
12.591414131
< 0.1%
12.395708081
< 0.1%
11.055277161
< 0.1%
10.587510731
< 0.1%

LDAPS_PPT4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1918
Distinct (%)25.0%
Missing75
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.26940735
Minimum0
Maximum16.65546921
Zeros5751
Zeros (%)74.2%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:49.491189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.15 × 10-5
95-th percentile1.295678829
Maximum16.65546921
Range16.65546921
Interquartile range (IQR)4.15 × 10-5

Descriptive statistics

Standard deviation1.206214071
Coefficient of variation (CV)4.477287169
Kurtosis54.31825132
Mean0.26940735
Median Absolute Deviation (MAD)0
Skewness6.792379039
Sum2068.240226
Variance1.454952386
MonotonicityNot monotonic
2021-08-15T09:30:49.660769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05751
74.2%
0.0019531253
 
< 0.1%
0.0010647272
 
< 0.1%
0.0011048952
 
< 0.1%
0.0005391122
 
< 0.1%
0.0007098182
 
< 0.1%
16.655469212
 
< 0.1%
4.6 × 10-52
 
< 0.1%
0.0019093222
 
< 0.1%
0.0079474461
 
< 0.1%
Other values (1908)1908
 
24.6%
(Missing)75
 
1.0%
ValueCountFrequency (%)
05751
74.2%
1.65 × 10-51
 
< 0.1%
1.79 × 10-51
 
< 0.1%
2.36 × 10-51
 
< 0.1%
2.99 × 10-51
 
< 0.1%
3.78 × 10-51
 
< 0.1%
4.02 × 10-51
 
< 0.1%
4.15 × 10-51
 
< 0.1%
4.6 × 10-52
 
< 0.1%
6.92 × 10-51
 
< 0.1%
ValueCountFrequency (%)
16.655469212
< 0.1%
15.252828571
< 0.1%
14.781933171
< 0.1%
13.998952811
< 0.1%
13.805737421
< 0.1%
12.542132141
< 0.1%
12.45032451
< 0.1%
12.278965391
< 0.1%
12.216176921
< 0.1%
12.055163351
< 0.1%

lat
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.54472152
Minimum37.4562
Maximum37.645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:49.832311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum37.4562
5-th percentile37.4697
Q137.5102
median37.5507
Q337.5776
95-th percentile37.6181
Maximum37.645
Range0.1888
Interquartile range (IQR)0.0674

Descriptive statistics

Standard deviation0.05035152325
Coefficient of variation (CV)0.001341107917
Kurtosis-0.8654162635
Mean37.54472152
Median Absolute Deviation (MAD)0.0405
Skewness0.08706183722
Sum291046.6812
Variance0.002535275894
MonotonicityNot monotonic
2021-08-15T09:30:49.965958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
37.57761240
16.0%
37.55071240
16.0%
37.5237620
8.0%
37.4697620
8.0%
37.5102620
8.0%
37.6181620
8.0%
37.4967620
8.0%
37.6046620
8.0%
37.5372620
8.0%
37.645311
 
4.0%
Other values (2)621
8.0%
ValueCountFrequency (%)
37.4562311
 
4.0%
37.4697620
8.0%
37.4832310
 
4.0%
37.4967620
8.0%
37.5102620
8.0%
37.5237620
8.0%
37.5372620
8.0%
37.55071240
16.0%
37.57761240
16.0%
37.6046620
8.0%
ValueCountFrequency (%)
37.645311
 
4.0%
37.6181620
8.0%
37.6046620
8.0%
37.57761240
16.0%
37.55071240
16.0%
37.5372620
8.0%
37.5237620
8.0%
37.5102620
8.0%
37.4967620
8.0%
37.4832310
 
4.0%

lon
Real number (ℝ≥0)

HIGH CORRELATION

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.9913972
Minimum126.826
Maximum127.135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:50.121539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum126.826
5-th percentile126.838
Q1126.937
median126.995
Q3127.042
95-th percentile127.099
Maximum127.135
Range0.309
Interquartile range (IQR)0.105

Descriptive statistics

Standard deviation0.07943510894
Coefficient of variation (CV)0.0006255156703
Kurtosis-0.6320691456
Mean126.9913972
Median Absolute Deviation (MAD)0.058
Skewness-0.2852130228
Sum984437.311
Variance0.006309936532
MonotonicityNot monotonic
2021-08-15T09:30:50.257139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
127.135311
 
4.0%
126.826311
 
4.0%
126.995310
 
4.0%
126.937310
 
4.0%
126.991310
 
4.0%
126.988310
 
4.0%
127.024310
 
4.0%
127.086310
 
4.0%
127.022310
 
4.0%
126.97310
 
4.0%
Other values (15)4650
60.0%
ValueCountFrequency (%)
126.826311
4.0%
126.838310
4.0%
126.891310
4.0%
126.909310
4.0%
126.91310
4.0%
126.927310
4.0%
126.937310
4.0%
126.938310
4.0%
126.955310
4.0%
126.97310
4.0%
ValueCountFrequency (%)
127.135311
4.0%
127.099310
4.0%
127.086310
4.0%
127.085310
4.0%
127.083310
4.0%
127.058310
4.0%
127.042310
4.0%
127.04310
4.0%
127.032310
4.0%
127.024310
4.0%

DEM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.86797227
Minimum12.37
Maximum212.335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:50.405743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum12.37
5-th percentile15.5876
Q128.7
median45.716
Q359.8324
95-th percentile208.507
Maximum212.335
Range199.965
Interquartile range (IQR)31.1324

Descriptive statistics

Standard deviation54.2797804
Coefficient of variation (CV)0.877348625
Kurtosis1.974088874
Mean61.86797227
Median Absolute Deviation (MAD)17.016
Skewness1.723257137
Sum479600.521
Variance2946.29456
MonotonicityNot monotonic
2021-08-15T09:30:50.563355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
12.37311
 
4.0%
212.335311
 
4.0%
146.554310
 
4.0%
82.2912310
 
4.0%
54.6384310
 
4.0%
35.038310
 
4.0%
17.2956310
 
4.0%
52.518310
 
4.0%
75.0924310
 
4.0%
15.5876310
 
4.0%
Other values (15)4650
60.0%
ValueCountFrequency (%)
12.37311
4.0%
15.5876310
4.0%
17.2956310
4.0%
19.5844310
4.0%
21.9668310
4.0%
26.298310
4.0%
28.7310
4.0%
30.0464310
4.0%
30.968310
4.0%
33.3068310
4.0%
ValueCountFrequency (%)
212.335311
4.0%
208.507310
4.0%
146.554310
4.0%
132.118310
4.0%
82.2912310
4.0%
75.0924310
4.0%
59.8324310
4.0%
56.4448310
4.0%
54.6384310
4.0%
53.4712310
4.0%

Slope
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.257048466
Minimum0.0984746
Maximum5.17823
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:50.750820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0984746
5-th percentile0.1332
Q10.2713
median0.618
Q31.7678
95-th percentile4.7296
Maximum5.17823
Range5.0797554
Interquartile range (IQR)1.4965

Descriptive statistics

Standard deviation1.370443515
Coefficient of variation (CV)1.090207381
Kurtosis1.621799971
Mean1.257048466
Median Absolute Deviation (MAD)0.4626
Skewness1.56302
Sum9744.639705
Variance1.878115428
MonotonicityNot monotonic
2021-08-15T09:30:50.897460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.697310
 
4.0%
0.2661310
 
4.0%
0.6233310
 
4.0%
2.785310
 
4.0%
0.5721310
 
4.0%
0.0985310
 
4.0%
0.4125310
 
4.0%
2.6865310
 
4.0%
0.2223310
 
4.0%
0.1554310
 
4.0%
Other values (17)4652
60.0%
ValueCountFrequency (%)
0.09847461
 
< 0.1%
0.0985310
4.0%
0.1332310
4.0%
0.1457310
4.0%
0.1554310
4.0%
0.2223310
4.0%
0.2661310
4.0%
0.2713310
4.0%
0.4125310
4.0%
0.5055310
4.0%
ValueCountFrequency (%)
5.178231
 
< 0.1%
5.1782310
4.0%
4.7296310
4.0%
2.785310
4.0%
2.6865310
4.0%
2.5348310
4.0%
2.2579310
4.0%
1.7678310
4.0%
1.5629310
4.0%
1.2313310
4.0%

Solar radiation
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1575
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5341.502803
Minimum4329.520508
Maximum5992.895996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:51.054042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4329.520508
5-th percentile4554.965332
Q14999.018555
median5436.345215
Q35728.316406
95-th percentile5854.641602
Maximum5992.895996
Range1663.375488
Interquartile range (IQR)729.297851

Descriptive statistics

Standard deviation429.158867
Coefficient of variation (CV)0.08034421825
Kurtosis-0.9716805418
Mean5341.502803
Median Absolute Deviation (MAD)333.51709
Skewness-0.5112097191
Sum41407329.73
Variance184177.3331
MonotonicityNot monotonic
2021-08-15T09:30:51.222590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5818.5268555
 
0.1%
4926.9580085
 
0.1%
4671.6464845
 
0.1%
5652.8105475
 
0.1%
5161.9414065
 
0.1%
5824.2597665
 
0.1%
4443.3139655
 
0.1%
4602.1181645
 
0.1%
4868.3422855
 
0.1%
4596.4208985
 
0.1%
Other values (1565)7702
99.4%
ValueCountFrequency (%)
4329.5205082
 
< 0.1%
4365.4316411
 
< 0.1%
4371.684575
0.1%
4393.8828131
 
< 0.1%
4395.1655271
 
< 0.1%
4398.2636721
 
< 0.1%
4398.5732421
 
< 0.1%
4400.3427731
 
< 0.1%
4401.7055661
 
< 0.1%
4403.2729491
 
< 0.1%
ValueCountFrequency (%)
5992.8959965
0.1%
5987.718755
0.1%
5981.9794925
0.1%
5975.676275
0.1%
5968.8090825
0.1%
5961.3774415
0.1%
5953.3769535
0.1%
5944.7788095
0.1%
5935.6152345
0.1%
5932.1240234
0.1%

Next_Tmax
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct183
Distinct (%)2.4%
Missing27
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean30.27488673
Minimum17.4
Maximum38.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:51.408062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum17.4
5-th percentile24.8
Q128.2
median30.5
Q332.6
95-th percentile34.9
Maximum38.9
Range21.5
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation3.128010058
Coefficient of variation (CV)0.1033202894
Kurtosis-0.2541201027
Mean30.27488673
Median Absolute Deviation (MAD)2.2
Skewness-0.3396071784
Sum233873.5
Variance9.784446922
MonotonicityNot monotonic
2021-08-15T09:30:51.577641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.3113
 
1.5%
33106
 
1.4%
31.3104
 
1.3%
29.6101
 
1.3%
31.298
 
1.3%
29.496
 
1.2%
29.795
 
1.2%
30.295
 
1.2%
29.294
 
1.2%
32.594
 
1.2%
Other values (173)6729
86.8%
ValueCountFrequency (%)
17.42
< 0.1%
18.61
 
< 0.1%
18.91
 
< 0.1%
19.51
 
< 0.1%
19.71
 
< 0.1%
20.11
 
< 0.1%
20.33
< 0.1%
20.42
< 0.1%
20.51
 
< 0.1%
20.61
 
< 0.1%
ValueCountFrequency (%)
38.92
< 0.1%
38.71
 
< 0.1%
38.31
 
< 0.1%
37.91
 
< 0.1%
37.83
< 0.1%
37.61
 
< 0.1%
37.51
 
< 0.1%
37.41
 
< 0.1%
37.31
 
< 0.1%
37.23
< 0.1%

Next_Tmin
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct157
Distinct (%)2.0%
Missing27
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean22.93222006
Minimum11.3
Maximum29.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.7 KiB
2021-08-15T09:30:51.793032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum11.3
5-th percentile18.6
Q121.3
median23.1
Q324.6
95-th percentile26.8
Maximum29.8
Range18.5
Interquartile range (IQR)3.3

Descriptive statistics

Standard deviation2.487612771
Coefficient of variation (CV)0.108476753
Kurtosis0.2517230525
Mean22.93222006
Median Absolute Deviation (MAD)1.6
Skewness-0.4037428033
Sum177151.4
Variance6.1882173
MonotonicityNot monotonic
2021-08-15T09:30:51.974580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.5159
 
2.1%
23.4157
 
2.0%
24156
 
2.0%
23.2150
 
1.9%
23.8149
 
1.9%
23.9142
 
1.8%
23.7135
 
1.7%
23.6133
 
1.7%
22.1133
 
1.7%
23.1129
 
1.7%
Other values (147)6282
81.0%
ValueCountFrequency (%)
11.32
< 0.1%
12.91
 
< 0.1%
13.31
 
< 0.1%
13.41
 
< 0.1%
13.61
 
< 0.1%
13.82
< 0.1%
13.91
 
< 0.1%
14.13
< 0.1%
14.31
 
< 0.1%
14.42
< 0.1%
ValueCountFrequency (%)
29.82
 
< 0.1%
29.51
 
< 0.1%
29.41
 
< 0.1%
29.11
 
< 0.1%
291
 
< 0.1%
28.83
< 0.1%
28.73
< 0.1%
28.63
< 0.1%
28.52
 
< 0.1%
28.46
0.1%

Interactions

2021-08-15T09:28:39.046462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:28:39.349648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:28:39.598983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-08-15T09:30:00.055387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:00.432380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:00.624864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:00.830348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:01.004847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:01.198328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:01.420734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:01.648127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:01.876514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:02.131833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:02.422055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:02.688342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:03.024444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:03.421382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:03.762468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:04.008811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:04.256149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:04.482577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:04.675066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:04.859573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:05.070967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:05.273463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:05.489846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:05.716241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:05.927713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:06.153074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:06.360553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:06.567962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:06.744491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:07.072613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:07.266094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:07.457611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:07.634109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:07.847540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:08.047042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:08.233506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:08.483836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:08.704246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:08.924664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:09.122132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:09.317606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:09.502111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:09.703608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:09.900048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:10.113477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:10.344858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:10.522419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:10.732855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:10.932288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:11.128760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:11.328262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:11.532681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:11.706217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:11.995442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:12.192914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:12.399368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:12.610830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:12.813255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:12.986825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:13.171297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:13.383730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:13.574218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:13.738814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:13.927278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:14.094860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:14.265369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:14.432920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:14.595485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:14.762075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:14.953567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:15.130055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:15.317553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:15.494084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:15.675596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:15.845180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:16.026692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:16.184234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:16.366781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:16.521334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:16.705873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:16.887390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:17.060924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:17.211521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:17.360089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:17.528638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:17.696189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:17.846788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:17.999379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:18.162943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:18.353433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:18.514001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:18.662638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:18.848109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:19.053558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:19.235075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:19.422571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:19.727756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:19.972140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:20.228416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:20.686192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:21.009328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:21.390307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:21.759320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:22.161258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:22.558183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:22.999004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:23.343084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:23.610368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:23.973402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:24.307502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:24.550852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:24.839115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:25.109356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:25.763607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:26.015932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:26.237341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:26.463735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:26.843717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:27.112998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:27.415190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:27.735331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:27.960730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:28.272893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:28.604016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:28.904204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:29.154534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:29.343030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:29.552469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:29.712045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:29.940432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:30.148875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:30.331386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:30.562767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:30.799135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:31.069412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:31.387561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:31.621934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:31.859300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:32.070733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:32.360958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:32.639213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:33.039141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:33.350310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:33.657489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:33.879894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:34.094332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:34.312736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:34.537133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:34.701693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:34.880218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:35.084671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:35.294153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:35.559399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:35.754911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:35.960370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:36.135891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:36.332331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:36.534791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:36.727309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:36.915770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:37.099314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:37.299743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:37.515167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:37.691694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:37.862237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:38.047743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:38.226298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:38.421777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:38.603255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:38.789756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:38.960300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:39.150790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-08-15T09:30:39.342278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-08-15T09:30:52.245059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-15T09:30:52.697849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-15T09:30:53.161644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-15T09:30:53.623373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-08-15T09:30:39.704311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-15T09:30:40.844565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-08-15T09:30:41.463545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-08-15T09:30:42.299963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

stationDatePresent_TmaxPresent_TminLDAPS_RHminLDAPS_RHmaxLDAPS_Tmax_lapseLDAPS_Tmin_lapseLDAPS_WSLDAPS_LHLDAPS_CC1LDAPS_CC2LDAPS_CC3LDAPS_CC4LDAPS_PPT1LDAPS_PPT2LDAPS_PPT3LDAPS_PPT4latlonDEMSlopeSolar radiationNext_TmaxNext_Tmin
01.06/30/201328.721.458.25568891.11636428.07410123.0069366.81888769.4518050.2339470.2038960.1616970.1309280.00.00.00.037.6046126.991212.33502.78505992.89599629.121.2
12.06/30/201331.921.652.26339790.60472129.85068924.0350095.69189051.9374480.2255080.2517710.1594440.1277270.00.00.00.037.6046127.03244.76240.51415869.31250030.522.5
23.06/30/201331.623.348.69047983.97358730.09129224.5656336.13822420.5730500.2093440.2574690.2040910.1421250.00.00.00.037.5776127.05833.30680.26615863.55566431.123.9
34.06/30/201332.023.458.23978896.48368829.70462923.3261775.65005065.7271440.2163720.2260020.1611570.1342490.00.00.00.037.6450127.02245.71602.53485856.96484431.724.3
45.06/30/201331.421.956.17409590.15512829.11393423.4864805.735004107.9655350.1514070.2499950.1788920.1700210.00.00.00.037.5507127.13535.03800.50555859.55224631.222.5
56.06/30/201331.923.552.43712685.30725129.21934223.8226136.18229550.2313890.1852790.2808180.2328410.1463630.00.00.00.037.5102127.04254.63840.14575873.78076231.524.0
67.06/30/201331.424.456.28718981.01976028.55185924.2384675.587135125.1100070.3896000.3335700.2704190.1457050.00.00.00.037.5776126.83812.37000.09855849.23339830.923.4
78.06/30/201332.123.652.32621878.00453928.85198223.8190546.10441742.0115470.3578560.3449280.2723870.1438410.00.00.00.037.4697126.91052.51801.56295863.99218831.122.9
89.06/30/201331.422.055.33879180.78460728.42697523.3323736.01713585.1109710.4087660.3448000.2649800.1489110.00.00.00.037.4967126.82650.93120.41255876.90136731.321.6
910.06/30/201331.620.556.65120386.84963227.57670522.5270186.51884163.0060750.3482920.3062550.2452540.1354430.00.00.00.037.4562126.955208.50705.17825893.60839830.521.0

Last rows

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774218.08/30/201723.318.230.25968286.56519326.47440317.2891576.52273194.2037830.0454510.0343400.000000e+000.0140700.0000000.0000000.0000000.00000037.4832127.02456.44481.2313004435.61181627.816.7
774319.08/30/201722.615.025.53604578.77904526.59628017.8560406.90596354.0300750.0566340.0762890.000000e+000.0000000.0000000.0000000.0000000.00000037.5776126.93875.09241.7678004495.47363327.116.6
774420.08/30/201722.715.938.21637394.42811624.03722215.7726776.478060112.5803100.0380190.0053800.000000e+000.0009380.0000000.0000000.0000000.00000037.6181127.004146.55404.7296004554.96533226.716.3
774521.08/30/201723.117.824.68899778.26138327.81269718.3030146.6032539.6140740.0529020.0301690.000000e+000.0043800.0000000.0000000.0000000.00000037.5507127.04026.29800.5721004456.02441427.617.7
774622.08/30/201722.517.430.09485883.69001826.70490517.8140385.76808382.1467070.0664610.0245185.850000e-070.0176780.0000000.0000000.0000000.00000037.5102127.08621.96680.1332004441.80371128.017.1
774723.08/30/201723.317.126.74131078.86985826.35208118.7756786.14891872.0582940.0300340.0810350.000000e+000.0000000.0000000.0000000.0000000.00000037.5372126.89115.58760.1554004443.31396528.318.1
774824.08/30/201723.317.724.04063477.29497527.01019318.7335196.54281947.2414570.0358740.0749620.000000e+000.0000000.0000000.0000000.0000000.00000037.5237126.90917.29560.2223004438.37353528.618.8
774925.08/30/201723.217.422.93301477.24374427.93951618.5229657.2892649.0900340.0489540.0598690.000000e+000.0007960.0000000.0000000.0000000.00000037.5237126.97019.58440.2713004451.34521527.817.4
7750NaNNaN20.011.319.79466658.93628317.62495414.2726462.882580-13.6032120.0000000.0000000.000000e+000.0000000.0000000.0000000.0000000.00000037.4562126.82612.37000.0984754329.52050817.411.3
7751NaNNaN37.629.998.524734100.00015338.54225529.61934221.857621213.4140060.9672770.9683539.837888e-010.97471023.70154421.62166115.84123516.65546937.6450127.135212.33505.1782305992.89599638.929.8